Significant advancement in imaging sensors, microscopes, digital cameras, and digital imaging devices coupled with high speed network connection and large storage devices enables broad new applications in image based measurement, analyses, and decisions. Patterns of interest on images depending on object properties, imaging conditions and application requirements. Therefore, they could vary significantly among applications. Recognize and extract patterns of interest from images have been a longstanding challenge for a vast majority of the imaging applications. Except for simple applications where patterns could be extracted by simple intensity threshold, manual drawing or extensive custom programming is often required for image pattern extraction. Manual drawing is tedious and irreproducible. Custom programming requires significant image processing and programming knowledge that is neither available nor cost effective for most of practical applications. The lack of adequate solutions to the recognition and extraction of patterns from images represents a critical bottleneck in productive applications of image based measurement and analyses.
It is highly desirable to have a general purpose solution that allows user to flexibly direct the computer to recognize and to extract the wide variety of patterns of interest without any knowledge of image processing and programming. The solution has to be fast in execution and has to be robust that could yield good results over a large number of images. The solution should also allow incremental updates and changes of the recognition instructions by human.
Prior art image pattern extraction or object segmentation methods are performed in a primitive and ad-hoc fashion on almost all image processing systems. For simple applications, image thresholding is the standard method for pattern segmentation. This works on images containing bright objects against dark background or dark objects against bright background. In this case, the pattern segmentation methods amount to determining a suitable threshold value to separate objects from background (Haralick R M and Shapiro, L G, “Survey Image Segmentation Techniques,” Comput Vision, Graphics Image Processing, vol. 29: 100-132, 1985; Otsu N, “A Threshold Selection Method for Gray-level Histograms,” IEEE Trans. System Man and Cybernetics, vol. SMC-9, No. 1, January 1979, PP 62-66). For images with multiple object types with high object boundary contrast, edge detection methods are often used for object segmentation. (Lee, J S J, Haralick, R M and Shapiro, L G, “Morphologic Edge Detection,” IEEE Trans. Robotics and Automation RA3(2):142-56, 1987.) However, the above methods are only applicable to easy cases with high signal contrast and low noise. They fail miserably on more challenging applications where the pattern of interest is not simple and the signal to noise is not high.
Application specific pattern extraction methods were developed for complicated yet well-defined and high volume applications such as blood cell counting, Pap smear screening, and semiconductor inspection. Human with image processing expertise through extensive programming and trial and error process that involves not only object segmentation module but also optics, illumination, and image acquisition process adjustments developed the application specific object segmentation methods. For complicated yet not well-defined or low volume applications, automatic pattern extraction method doe not exist.